In the current information age there are various forms of databases used to store data. Different types of databases employ different data storage models. Depending on the type of data collected and access requirements of stored data, a designer may select an appropriate database type and implementation design. The implementation design addresses concerns regarding whether or not the database is distributed, internal tuning parameters of a database, redundancy of data storage, and hardware specifications for the infrastructure supporting the database, etc.
A relational database typically allows for the definition of data structures, storage and retrieval operations and integrity constraints. In a relational database the data and relations between them are organized in tables. A table is a collection of rows or records and each row in a table contains the same fields. Certain fields may be designated as keys, which means that searches for specific values of that field can use indexing to speed them up. Where fields in two different tables take values from the same set, a join operation can be performed to select related records in the two tables by matching values in those fields. Often, but not always, the fields will have the same name in both tables. For example, an “orders” table night contain (customer_id, product_code) pairs and a “products” table might contain (product_code, price) pairs so to calculate a given customer's bill you would sum the prices of all products ordered by that customer by joining on the product-code fields of the two tables. This can be extended to joining multiple tables on multiple fields. Because these relationships are only specified at retrieval time, relational databases are classed as dynamic database management system.
A time series database (regular) is a software system that is optimized for handling time series data, arrays of numbers indexed by time (a date time or a date time range). In a regular time series database a sequence of data points are measured at successive points in time and spaced at uniform time intervals. In a slightly different data model an “irregular” time series database allows for time series data to be collected over time at non-uniform time intervals.
With the advent of Big Data, problems faced by database designers have become even more complex. Big Data storage requirements are on a magnitude not contemplated by traditional database architectures. Disclosed herein are systems and methods to increase performance and maintain proper visibility into a distributed database of time stamped records, particularly when utilized to store Big Data quantities of event records as events occur on the Internet.